SPP1 and CAR-treated mouse tumors: Annotation and basic analysis

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Last updated: 2025-09-18

Checks: 5 2

Knit directory: SPP1_mouse_scRNAseq/

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/home/hnatri/SPP1_mouse_scRNAseq/code/colors_themes.R	code/colors_themes.R
/home/hnatri/SPP1_mouse_scRNAseq/cluster_sig_top_markers_nCount1k_nFeat500_dblrate15.tsv	cluster_sig_top_markers_nCount1k_nFeat500_dblrate15.tsv

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/first_pass.Rmd) and HTML (docs/first_pass.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	ff67aa5	heinin	2025-09-16	Updated CellChat plots
Rmd	f226445	heinin	2025-07-14	Added lymphoid marker expression
html	f226445	heinin	2025-07-14	Added lymphoid marker expression
Rmd	fa32c56	heinin	2025-02-28	Analyzing compartments
html	fa32c56	heinin	2025-02-28	Analyzing compartments
Rmd	84ba8d7	heinin	2025-01-07	More featureplots
html	84ba8d7	heinin	2025-01-07	More featureplots
Rmd	fa70de3	heinin	2025-01-07	Small updates
html	fa70de3	heinin	2025-01-07	Small updates
Rmd	a3864c6	heinin	2025-01-07	First pass analysis
html	a3864c6	heinin	2025-01-07	First pass analysis

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Libraries and environment variables

library(workflowr)
library(Seurat)
library(googlesheets4)
library(tidyverse)
library(plyr)
library(UpSetR)
library(ggrepel)
library(enrichR)
library(patchwork)
library(biomaRt)
library(scProportionTest)

setwd("/home/hnatri/SPP1_mouse_scRNAseq/")
set.seed(1234)
options(future.globals.maxSize = 30000 * 1024^2)
reduction <- "integratedSCTumap"

source("/home/hnatri/SPP1_mouse_scRNAseq/code/CART_plot_functions.R")
source("/home/hnatri/SPP1_mouse_scRNAseq/code/colors_themes.R")
source("/home/hnatri/SingleCellBestPractices/scripts/preprocessing_qc_module.R")
source("/home/hnatri/SingleCellBestPractices/scripts/integration_module.R")

# Cluster annotations
gs4_deauth()
cluster_annot  <- gs4_get("https://docs.google.com/spreadsheets/d/127J6C4KF7uBGKUnrPuC1mcsb_wNCN6k1zXKSCbJ6q0M/edit?usp=sharing")
cluster_annot <- read_sheet(cluster_annot, sheet = "Cluster annotation")

Helper functions

Import data

Core ran CCA integration

seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/ROUND1/Seurat.rds")
unique(seurat_data$orig.ident)

DimPlot(seurat_data, reduction = "integrated.cca")

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
a3864c6	heinin	2025-01-07

QC

VlnPlot(seurat_data, features = c("nCount_RNA", "nFeature_RNA", "percent.mt"),
        pt.size = 0)

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Version	Author	Date
a3864c6	heinin	2025-01-07

par(mfrow=c(2,3))

# PLOTS 1 & 2: nCount vs. nFeature
smoothScatter(log2(seurat_data$nCount_RNA), log2(seurat_data$nCount_RNA),
              xlab = "log2(nCount_RNA)", ylab = "log2(nFeature_RNA)")

smoothScatter(seurat_data$nCount_RNA, seurat_data$nCount_RNA,
              xlab = "nCount_RNA", ylab = "nFeature_RNA")

# PLOTS 3 & 4: nCount vs. percent.mt_RNA
smoothScatter(seurat_data$percent.mt, log2(seurat_data$nCount_RNA),
              xlab = "% MT", ylab = "log2(nCount_RNA)")

smoothScatter(seurat_data$percent.mt, seurat_data$nCount_RNA,
              xlab = "% MT", ylab = "nCount_RNA")
abline(v = 10, h = 1000, 
       lty = "dashed", lwd = 1.25, col = "red")

# PLOTS 5 & 6: nFeature vs. percent.mt_RNA
smoothScatter(seurat_data$percent.mt, log2(seurat_data$nFeature_RNA),
              xlab = "% MT", ylab = "log2(nFeature_RNA)")

smoothScatter(seurat_data$percent.mt, seurat_data$nFeature_RNA,
              xlab = "% MT", ylab = "nFeature_RNA")
abline(v = 10, h = 500, 
       lty = "dashed", lwd = 1.25, col = "red")

Clustering and UMAP construction

seurat_data <- subset(seurat_data, subset = nFeature_RNA > 500 & nCount_RNA > 1000)

# Normalizing and scaling
seurat_data <- SCTransform(seurat_data,
                           variable.features.n = 1000,
                           #vars.to.regress = c(""),
                           vst.flavor = "v2",
                           return.only.var.genes = T,
                           verbose = F)

# Adding cell cycle scores
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes

# Converting human gene names to mouse
mouse_human_genes <- read.table("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt",
                                sep="\t", header = T)

mouse <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[2]]
human <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[1]]
mouse <- mouse[,c(1,4)]
human <- human[,c(1,4)]
mh_data <- merge.data.frame(mouse, human, by = "DB.Class.Key",all.y = TRUE)

s.genes <- mh_data %>% filter(Symbol.y %in% s.genes) %>%
  dplyr::select(Symbol.x) %>% unlist() %>% as.character()
g2m.genes <- mh_data %>% filter(Symbol.y %in% g2m.genes) %>%
  dplyr::select(Symbol.x) %>% unlist() %>% as.character()

seurat_data <- CellCycleScoring(seurat_data,
                                s.features = s.genes,
                                g2m.features = g2m.genes,
                                set.ident = F)

### Rerunning integration
vars_to_regress <- c("G2M.Score",
                     "S.Score",
                     "nCount_RNA",
                     "nFeature_RNA",
                     "percent.mt")

DefaultAssay(seurat_data) <- "RNA"
seurat_data <- JoinLayers(seurat_data)
seurat_list <- SplitObject(seurat_data, split.by = "orig.ident")

# Unable to run SoupX: no raw data files

# Calling doublets
seurat_list <- run_sctransform(seurat_list)
seurat_list <- run_doubletfinder(sample_seurat_list = seurat_list,
                                 manual_dbr = 0.15)

# Removing doublets
seurat_list <- lapply(seurat_list, function(xx){
  subset(xx, subset = doublet_finder == "Singlet")
})

# Integrating
seurat_list <- run_sctransform(seurat_list,
                               vars_to_regress = vars_to_regress)
integrated_data <- sct_rpca_integration(seurat_list,
                                        ndims = 8)

saveRDS(integrated_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds")

Visualizing clusters, marker expression

# range(integrated_data$nCount_RNA)
# 500 122405
# range(integrated_data$nFeature_RNA)
# 217 8992

integrated_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds")
integrated_data$cluster <- integrated_data$integratedSCTsnn_res.0.3
table(integrated_data$cluster)

range(integrated_data$nCount_RNA)
range(integrated_data$nFeature_RNA)

DimPlot(integrated_data,
        group.by = "cluster",
        cols = carspp1_cluster_col,
        reduction = reduction,
        label = T,
        label.box = T,
        label.size = 3,
        repel = T,
        raster = T,
        raster.dpi = c(1024, 1024),
        pt.size = 3) +
  ggtitle("integratedSCTsnn_res.0.3") +
  theme_classic() +
  NoLegend() +
  NoAxes() +
  coord_fixed(1)

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
a3864c6	heinin	2025-01-07

DimPlot(integrated_data,
        group.by = "orig.ident",
        #cols = jak1_celltype_col,
        reduction = reduction,
        #label = T,
        #label.box = T,
        #label.size = 3,
        #repel = T,
        raster = T,
        raster.dpi = c(1024, 1024),
        pt.size = 3) +
  ggtitle("Sample") +
  theme_classic() +
  #NoLegend() +
  NoAxes() +
  coord_fixed(1)

Past versions of unnamed-chunk-7-2.png

Version	Author	Date
a3864c6	heinin	2025-01-07

DimPlot(integrated_data,
        group.by = "Phase",
        #cols = jak1_celltype_col,
        reduction = reduction,
        #label = T,
        #label.box = T,
        #label.size = 3,
        #repel = T,
        raster = T,
        raster.dpi = c(1024, 1024),
        pt.size = 3) +
  ggtitle("Phase") +
  theme_classic() +
  #NoLegend() +
  NoAxes() +
  coord_fixed(1)

Past versions of unnamed-chunk-7-3.png

Version	Author	Date
a3864c6	heinin	2025-01-07

DimPlot(integrated_data,
        group.by = "cluster",
        split.by = "orig.ident",
        cols = carspp1_cluster_col,
        reduction = reduction,
        ncol = 2,
        label = T,
        label.box = T,
        label.size = 3,
        repel = T,
        raster = T,
        raster.dpi = c(1024, 1024),
        pt.size = 3) +
  ggtitle("") +
  theme_classic() +
  NoLegend() +
  NoAxes() +
  coord_fixed(1)

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
a3864c6	heinin	2025-01-07

Marker expression and cell type annotation

# Converting mouse gene names to human
mouse_human_genes <- read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt", sep="\t")

convert_mouse_to_human <- function(gene_list){
  gene_names <- as.data.frame(matrix(nrow = length(gene_list),
                                     ncol = 2))
  colnames(gene_names) <- c("mouse", "human")
  rownames(gene_names) <- gene_list
  gene_names$mouse <- gene_list
  
  for(gene in gene_list){
    class_key = (mouse_human_genes %>% filter(Symbol == gene & Common.Organism.Name=="mouse, laboratory"))[['DB.Class.Key']]
    if(!identical(class_key, integer(0)) ){
      human_genes = (mouse_human_genes %>% filter(DB.Class.Key == class_key & Common.Organism.Name=="human"))[,"Symbol"]
      
      if(length(human_genes)==0){
        gene_names[gene, "human"] <- NA
      } else if (length(human_genes)>1){
        #  human_genes <- paste0(human_genes, collapse = ", ")
        bind_df <- data.frame("mouse" = rep(gene, times = length(human_genes)),
                              "human" = human_genes)
        gene_names <- rbind(gene_names, bind_df)
      } else {
        gene_names[gene, "human"] <- human_genes
      }
    }
  }
  return(gene_names)
}

gene_names <- convert_mouse_to_human(rownames(integrated_data@assays$RNA))

length(rownames(integrated_data@assays$RNA))
dim(gene_names)

# Keeping mouse genes with a single human ortholog
gene_names <- gene_names %>%
  group_by(mouse) %>%
  filter(!is.na(human),
         n() == 1) %>%
  ungroup()

DefaultAssay(integrated_data) <- "RNA"
integrated_data <- JoinLayers(integrated_data)
assay_data <- GetAssayData(integrated_data, slot = "counts")
assay_data <- assay_data[which(rownames(assay_data) %in% gene_names$mouse),]
new_names <- rownames(assay_data)
new_names <- mapvalues(x = new_names,
                       from = gene_names$mouse,
                       to = gene_names$human)
rownames(assay_data) <- new_names

integrated_data[["RNA_human"]] <- CreateAssayObject(assay_data,
                                                    min.cells = 0,
                                                    min.features = 0)
DefaultAssay(integrated_data) <- "RNA_human"
integrated_data <- NormalizeData(integrated_data)

saveRDS(integrated_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds")

FeaturePlot(integrated_data,
            #layer = "RNA",
            slot = "data",
            features = c("PTPRC", "CD3E", "CD4", "CD8A", "CD19", "ITGAM",
                         "CD36", "CD14", "SPP1", "C1QA", "C1QB", "C1QC",
                         "APOE", "SPP1"),
            order = T,
            ncol = 5,
            reduction = reduction,
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
f226445	heinin	2025-07-14
84ba8d7	heinin	2025-01-07
a3864c6	heinin	2025-01-07

FeaturePlot(integrated_data,
            #layer = "RNA",
            slot = "data",
            features = c("G2M.Score", "S.Score", "nCount_RNA", "nFeature_RNA", "percent.mt"),
            order = T,
            ncol = 3,
            reduction = reduction,
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
a3864c6	heinin	2025-01-07

Using sc-ImmuCC to annotate immune cells

count_data <- LayerData(seurat_data, assay = "RNA_human", layer = "counts")

scImmuCC_Layered(count = count_data, Non_Immune = FALSE)

# Importing results
scicc_labels <- read.csv("/home/hnatri/PD1_mm/docs/Layer1_scImmuCC_label.csv",
                         row.names = "X")

length(colnames(seurat_data))
length(intersect(scicc_labels$barcodes, colnames(seurat_data)))

seurat_data$scImmuCC_celltype <- mapvalues(x = colnames(seurat_data),
                                           from = scicc_labels$barcodes,
                                           to = scicc_labels$cell_type)

# Plotting
DimPlot(seurat_data,
        group.by = "scImmuCC_celltype",
        reduction = "umap",
        raster = T,
        #cols = scImmuCC_celltype_col,
        label = T) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend() &
  manuscript_theme

#saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")

Top markers and cell type annotations

unique(integrated_data$cluster)
carspp1_cluster_col

markers <- presto::wilcoxauc(integrated_data,
                             group_by = "cluster",
                             assay = "data",
                             seurat_assay = "RNA_human")

#markers <- markers[-which(markers$feature %in% grep("MT-", markers$feature, value = T)),]
#markers <- markers[-which(markers$feature %in% grep("^RP", markers$feature, value = T)),]

markers_sig <- markers %>%
  filter(padj < 0.01,
         abs(logFC) > 1)

write.table(markers_sig, "/home/hnatri/SPP1_mouse_scRNAseq/cluster_sig_top_markers_nCount1k_nFeat500_dblrate15.tsv", quote = F, row.names = F, sep = "\t")

plot_features <- markers %>%  group_by(group) %>% slice_max(order_by = auc, n = 5)

create_dotplot_heatmap(seurat_object = integrated_data,
                       plot_features = unique(plot_features$feature),
                       group_var = "cluster",
                       group_colors = carspp1_cluster_col,
                       column_title = "",
                       row_km = 5,
                       col_km = 5,
                       row.order = NULL,
                       col.order = NULL)

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Version	Author	Date
f226445	heinin	2025-07-14
84ba8d7	heinin	2025-01-07
a3864c6	heinin	2025-01-07

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f226445	heinin	2025-07-14
84ba8d7	heinin	2025-01-07
a3864c6	heinin	2025-01-07

TME markers

plot_features <- c("PTMA", "PFN1", "CFL1", "TMSB4X", "TPT1", "TMSB10", "MIF",
                   "PDPN", "NLRP3", "IL1B", "CCL4", "S100A8", "S100A9",
                   "S100A10", "TYROBP", "CD68", "ICAM1", "C1QA", "C1QB", "C1QC",
                   "CD74", "AREG", "CD4", "APOE", "FABP5", "SPP1", "CD274",
                   "CD96", "PTPRC", "CEMIP2", "KLRD1", "CD8A", "NKG7", "IL32",
                   "CD3D", "BTG1", "IFITM2", "ITM2A", "SELL", "GZMB", "CD79A",
                   "ACTA2", "PDGFRB", "COL1A1", "CD163", "MRC1", "ITGAM", "CD14",
                   "CD279", "PDCD1", "TREM2", "TMEM119", "P2RY12", "CX3CR1",
                   "CD19")

create_dotplot_heatmap(seurat_object = integrated_data,
                       plot_features = plot_features,
                       group_var = "cluster",
                       group_colors = carspp1_cluster_col,
                       column_title = "",
                       row_km = 5,
                       col_km = 5,
                       row.order = NULL,
                       col.order = NULL)

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Version	Author	Date
f226445	heinin	2025-07-14
84ba8d7	heinin	2025-01-07
a3864c6	heinin	2025-01-07

FeaturePlot(integrated_data,
            #layer = "RNA",
            slot = "data",
            features = plot_features,
            order = T,
            ncol = 5,
            reduction = reduction,
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

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Version	Author	Date
84ba8d7	heinin	2025-01-07
fa70de3	heinin	2025-01-07
a3864c6	heinin	2025-01-07

Adding annotations

Idents(integrated_data) <- integrated_data$integratedSCTsnn_res.0.3
integrated_data <- FindSubCluster(integrated_data,
                                  cluster = 8,
                                  graph.name = "integratedSCTsnn",
                                  subcluster.name = "sub.cluster",
                                  resolution = 0.1,
                                  algorithm = 1)

DimPlot(integrated_data,
        group.by = "sub.cluster",
        #cols = carspp1_celltype_col,
        reduction = reduction,
        label = T,
        label.box = T,
        label.size = 3,
        repel = T,
        raster = T,
        raster.dpi = c(1024, 1024),
        pt.size = 3) +
  ggtitle("") +
  theme_classic() +
  NoLegend() +
  NoAxes() +
  coord_fixed(1)

integrated_data$annot <- mapvalues(x = integrated_data$sub.cluster,
                                   from = cluster_annot$sub.cluster,
                                   to = cluster_annot$annot)

DimPlot(integrated_data,
        group.by = "annot",
        cols = carspp1_celltype_col,
        reduction = reduction,
        label = T,
        label.box = T,
        label.size = 3,
        repel = T,
        raster = T,
        raster.dpi = c(1024, 1024),
        pt.size = 3) +
  ggtitle("") +
  theme_classic() +
  NoLegend() +
  NoAxes() +
  coord_fixed(1)

#saveRDS(integrated_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8.rds")

Cell type proportions

create_barplot(integrated_data,
               group_var = "orig.ident",
               plot_var = "annot",
               plot_levels = levels(integrated_data$annot),
               group_levels = sort(unique(integrated_data$orig.ident)),
               plot_colors = carspp1_celltype_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

Using scProportionTest. The first group (for example, CAR in “CAR vs. CTRL”) gets positive values.

prop_test <- sc_utils(integrated_data)

# Permutation testing and bootstrapping

# CAR vs. CTRL
prop_test <- permutation_test(
  prop_test, cluster_identity = "annot",
  sample_2 = "CAR", sample_1 = "TUMOR",
  sample_identity = "orig.ident")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("CAR vs. CTRL")

# SPP1 vs. CTRL
prop_test <- permutation_test(
  prop_test, cluster_identity = "annot",
  sample_2 = "SPP1", sample_1 = "TUMOR",
  sample_identity = "orig.ident")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1 vs. CTRL")

# CAR+SPP1 vs. CTRL
prop_test <- permutation_test(
  prop_test, cluster_identity = "annot",
  sample_2 = "SPP1+CAR", sample_1 = "TUMOR",
  sample_identity = "orig.ident")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1+CAR vs. CTRL")

# CAR+SPP1 vs. CAR
prop_test <- permutation_test(
  prop_test, cluster_identity = "annot",
  sample_2 = "SPP1+CAR", sample_1 = "CAR",
  sample_identity = "orig.ident")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1+CAR vs. CAR")

# CAR+SPP1 vs. SPP1
prop_test <- permutation_test(
  prop_test, cluster_identity = "annot",
  sample_2 = "SPP1+CAR", sample_1 = "SPP1",
  sample_identity = "orig.ident")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1+CAR vs. SPP1")

# To build on command line, run Rscript -e "rmarkdown::render('first_pass.Rmd')"
# Then "mv *.html /home/hnatri/SPP1_mouse_scRNAseq/docs/"

Session information

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
 [1] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] sctransform_0.4.1           ROCR_1.0-11                
 [3] KernSmooth_2.23-21          fields_14.1                
 [5] spam_2.9-1                  mclust_6.0.0               
 [7] scCustomize_1.1.1           glmGamPoi_1.14.3           
 [9] DoubletFinder_2.0.4         scater_1.30.1              
[11] scuttle_1.12.0              SingleCellExperiment_1.24.0
[13] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[15] GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
[17] IRanges_2.36.0              S4Vectors_0.40.2           
[19] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[21] matrixStats_1.0.0           SoupX_1.6.2                
[23] circlize_0.4.15             ComplexHeatmap_2.18.0      
[25] viridis_0.6.3               viridisLite_0.4.2          
[27] RColorBrewer_1.1-3          scProportionTest_0.0.0.9000
[29] biomaRt_2.58.2              patchwork_1.1.2            
[31] enrichR_3.2                 ggrepel_0.9.3              
[33] UpSetR_1.4.0                plyr_1.8.8                 
[35] lubridate_1.9.2             forcats_1.0.0              
[37] stringr_1.5.0               dplyr_1.1.2                
[39] purrr_1.0.1                 readr_2.1.4                
[41] tidyr_1.3.0                 tibble_3.2.1               
[43] ggplot2_3.4.2               tidyverse_2.0.0            
[45] googlesheets4_1.1.0         Seurat_5.0.1               
[47] SeuratObject_5.0.1          sp_1.6-1                   
[49] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.2                  spatstat.sparse_3.0-1    
  [3] bitops_1.0-7              httr_1.4.6               
  [5] doParallel_1.0.17         tools_4.3.0              
  [7] utf8_1.2.3                R6_2.5.1                 
  [9] lazyeval_0.2.2            uwot_0.1.14              
 [11] GetoptLong_1.0.5          withr_2.5.0              
 [13] prettyunits_1.1.1         gridExtra_2.3            
 [15] progressr_0.13.0          cli_3.6.1                
 [17] Cairo_1.6-0               spatstat.explore_3.2-1   
 [19] fastDummies_1.7.3         labeling_0.4.2           
 [21] sass_0.4.6                spatstat.data_3.0-1      
 [23] ggridges_0.5.4            pbapply_1.7-0            
 [25] parallelly_1.36.0         WriteXLS_6.4.0           
 [27] maps_3.4.1                rstudioapi_0.14          
 [29] RSQLite_2.3.1             generics_0.1.3           
 [31] shape_1.4.6               ica_1.0-3                
 [33] spatstat.random_3.1-5     Matrix_1.6-5             
 [35] ggbeeswarm_0.7.2          fansi_1.0.4              
 [37] abind_1.4-5               lifecycle_1.0.3          
 [39] whisker_0.4.1             yaml_2.3.7               
 [41] snakecase_0.11.0          SparseArray_1.2.3        
 [43] BiocFileCache_2.10.2      Rtsne_0.16               
 [45] paletteer_1.5.0           blob_1.2.4               
 [47] promises_1.2.0.1          crayon_1.5.2             
 [49] miniUI_0.1.1.1            lattice_0.21-8           
 [51] beachmat_2.18.1           cowplot_1.1.1            
 [53] KEGGREST_1.42.0           magick_2.7.4             
 [55] pillar_1.9.0              knitr_1.43               
 [57] rjson_0.2.21              future.apply_1.11.0      
 [59] codetools_0.2-19          leiden_0.4.3             
 [61] glue_1.6.2                getPass_0.2-4            
 [63] data.table_1.14.8         vctrs_0.6.2              
 [65] png_0.1-8                 cellranger_1.1.0         
 [67] gtable_0.3.3              rematch2_2.1.2           
 [69] cachem_1.0.8              xfun_0.39                
 [71] S4Arrays_1.2.0            mime_0.12                
 [73] survival_3.5-5            gargle_1.4.0             
 [75] iterators_1.0.14          ellipsis_0.3.2           
 [77] fitdistrplus_1.1-11       nlme_3.1-162             
 [79] bit64_4.0.5               progress_1.2.2           
 [81] filelock_1.0.2            RcppAnnoy_0.0.20         
 [83] rprojroot_2.0.3           bslib_0.4.2              
 [85] irlba_2.3.5.1             vipor_0.4.5              
 [87] colorspace_2.1-0          DBI_1.1.3                
 [89] ggrastr_1.0.2             tidyselect_1.2.0         
 [91] processx_3.8.1            bit_4.0.5                
 [93] compiler_4.3.0            curl_5.0.0               
 [95] git2r_0.32.0              BiocNeighbors_1.20.2     
 [97] xml2_1.3.4                DelayedArray_0.28.0      
 [99] plotly_4.10.2             scales_1.2.1             
[101] lmtest_0.9-40             callr_3.7.3              
[103] rappdirs_0.3.3            digest_0.6.31            
[105] goftest_1.2-3             presto_1.0.0             
[107] spatstat.utils_3.0-3      rmarkdown_2.22           
[109] XVector_0.42.0            htmltools_0.5.5          
[111] pkgconfig_2.0.3           sparseMatrixStats_1.14.0 
[113] highr_0.10                dbplyr_2.3.2             
[115] fastmap_1.1.1             rlang_1.1.1              
[117] GlobalOptions_0.1.2       htmlwidgets_1.6.2        
[119] DelayedMatrixStats_1.24.0 shiny_1.7.4              
[121] farver_2.1.1              jquerylib_0.1.4          
[123] zoo_1.8-12                jsonlite_1.8.5           
[125] BiocParallel_1.36.0       BiocSingular_1.18.0      
[127] RCurl_1.98-1.12           magrittr_2.0.3           
[129] GenomeInfoDbData_1.2.11   dotCall64_1.0-2          
[131] munsell_0.5.0             Rcpp_1.0.10              
[133] reticulate_1.29           stringi_1.7.12           
[135] zlibbioc_1.48.0           MASS_7.3-60              
[137] listenv_0.9.0             deldir_1.0-9             
[139] Biostrings_2.70.1         splines_4.3.0            
[141] tensor_1.5                hms_1.1.3                
[143] ps_1.7.5                  igraph_1.4.3             
[145] spatstat.geom_3.2-1       RcppHNSW_0.5.0           
[147] ScaledMatrix_1.10.0       reshape2_1.4.4           
[149] XML_3.99-0.14             evaluate_0.21            
[151] ggprism_1.0.4             tzdb_0.4.0               
[153] foreach_1.5.2             httpuv_1.6.11            
[155] RANN_2.6.1                polyclip_1.10-4          
[157] future_1.32.0             clue_0.3-64              
[159] scattermore_1.2           janitor_2.2.0            
[161] rsvd_1.0.5                xtable_1.8-4             
[163] RSpectra_0.16-1           later_1.3.1              
[165] googledrive_2.1.0         beeswarm_0.4.0           
[167] memoise_2.0.1             AnnotationDbi_1.64.1     
[169] cluster_2.1.4             timechange_0.2.0         
[171] globals_0.16.2